arXiv Computer Vision By Luis F. Gomez, Julian Fierrez, Roberto Daza, Ruben Tolosana, Aythami Morales, Gonzalo Garrido, Javier Rueda, Enrique Navarro

VideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running

Read the original on arXiv Computer Vision →

arXiv:2608. 19480v1 Announce Type: new Abstract: Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
Jun 10

Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization

arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.

By Parth Agrawal, Ronit, Sagar Kumar, Aashish Bhambri
arXiv Computer Vision
Sep 3

MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion

MuyBridge is an on-device system that estimates an athlete’s segmental center of mass (CoM) trajectory from a single phone camera video stream. It combines a compact 2D pose network with a distilled monocular depth network, fusing their outputs through anatomical and physical priors to produce metric CoM estimates without requiring 3D or task‑specific supervision. On the AthletePose3D dataset, MuyBridge achieves 33–41 mm vertical CoM error and 2.3–6.6 % absolute‑relative range error, delivering CoM estimates at 63 FPS on an iPhone 15 with asynchronous depth updates.

By Aidan Bradshaw, Marco Giordano, David Rode, Andreas Habersack, Elif Basokur, Annika Kruse, Markus Tilp, Michele Magno, Peter Wolf, Luca Benini, Christoph Leitner